Binarisation Algorithms Analysis on Document and Natural Scene Images

The binarisation plays an important role in a system for text extraction from images which is a prominent area in digital image processing. The primary goal of the binarisation techniques are to covert colored and gray scale image into black and white image so that overall computational overhead can be minimized. It has great impact on performance of the system for text extraction from image. Such system has number of applications like navigation system for visually impaired persons, automatic text extraction from document images, and number plate detection to enforcement traffic rules etc. The present study analysed the performance of well known binarisation algorithms on degraded documents and camera captured images. The statistical parameters namely Precession, Recall and F-measure and PSNR are used to evaluate the performance. To find the suitability of the binarisation method for text preservation in natural scene images, we have also considered visual observation DOI: 10.17762/ijritcc2321-8169.15083

BiNet:Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks

Handwritten document-image binarization is a semantic segmentation process to differentiate ink pixels from background pixels. It is one of the essential steps towards character recognition, writer identification, and script-style evolution analysis. The binarization task itself is challenging due to the vast diversity of writing styles, inks, and paper materials. It is even more difficult for historical manuscripts due to the aging and degradation of the documents over time. One of such manuscripts is the Dead Sea Scrolls (DSS) image collection, which poses extreme challenges for the existing binarization techniques. This article proposes a new binarization technique for the DSS images using the deep encoder-decoder networks. Although the artificial neural network proposed here is primarily designed to binarize the DSS images, it can be trained on different manuscript collections as well. Additionally, the use of transfer learning makes the network already utilizable for a wide range of handwritten documents, making it a unique multi-purpose tool for binarization. Qualitative results and several quantitative comparisons using both historical manuscripts and datasets from handwritten document image binarization competition (H-DIBCO and DIBCO) exhibit the robustness and the effectiveness of the system. The best performing network architecture proposed here is a variant of the U-Net encoder-decoders.Comment: 26 pages, 15 figures, 11 table

Automatic License Plate Recognition Using Deep Learning Techniques

Automatic License Plate Recognition (ALPR) systems capture a vehicles license plate and recognize the license number and other required information from the captured image. ALPR systems have number of significant applications: law enforcement, public safety agencies, toll gate systems, etc. The goal of these systems is to recognize the characters and state on the license plate with high accuracy. ALPR has been implemented using various techniques. Traditional recognition methods use handcrafted features for obtaining features from the image. Unlike conventional methods, deep learning techniques automatically select features and are one of the game changing technologies in the field of computer vision, automatic recognition tasks, natural language processing. Some of the most successful deep learning methods involve Convolutional Neural Networks. This research applies deep learning techniques to the ALPR problem of recognizing the state and license number from the USA license plate. Existing ALPR systems include three stages of processing: license plate localization, character segmentation and character recognition but do little for the state recognition problem. Our research not only extracts the license number, but also processes state information from the license plate. We also propose various techniques for further research in the field of ALPR using deep learning techniques

A novel license plate character segmentation method for different types of vehicle license plates.

License plate character segmentation (LPCS) is a very important part of vehicle license plate recognition (LPR) system. The accuracy of LPR system widely depends on two parts; namely license plate detection (LPD) and LPCS. Different country has different types and shapes of LPs are available. Based on character position on LP, we can find two types of LPs over the world, single row (SR) and double rows (DR) LP. Most of the LPCS methods are generally used for SRLP. This paper proposed a novel LPCS method for SR and DR types of LPs. Experimental results shows the real-time effectiveness of our proposed method. The accuracy of our proposed LPCS method is 99.05% and the average computational time is 27ms which is higher than other existing methods